Most visual features are parametric in nature, including edges, lines,
corners and junctions. We have developed an algorithm to automatically
construct detectors for arbitrary parametric features. To maximize robustness
we use realistic multi-parameter feature models and incorporate optical and
sensing effects. Each feature is represented as a densely sampled parametric
manifold in a low-dimensional subspace of a Hilbert space. During detection,
the vector of intensity values in a window about each pixel in the image is
projected into the subspace. If the projection lies sufficiently close to the
feature manifold, the feature is detected and the location of the closest
manifold point yields the feature parameters. The concepts of parameter
reduction by normalization, dimension reduction, pattern rejection and
heuristic search are all employed to achieve the required efficiency. Detectors
have been constructed for five features, namely, step edge (five parameters),
roof edge (five parameters), line (six parameters), corner (five parameters)
and circular disc (six parameters).